Configuring Genesys Cloud LLM Gateway Function Calling Definitions via REST API with Python

What You Will Build

• A Python module that constructs, validates, and persists LLM Gateway function calling definitions in Genesys Cloud using atomic PUT operations and optimistic locking.
• This uses the Genesys Cloud AI/LLM REST API surface and standard HTTP clients to manage function schemas, timeouts, and security policies.
• The code covers payload construction, JSON schema validation, rate-limit handling, webhook synchronization, latency tracking, audit logging, and a reusable configurator class.

Prerequisites

• OAuth 2.0 Client Credentials grant with scopes: ai:llm:function-definition:read, ai:llm:function-definition:write
• Genesys Cloud REST API v2
• Python 3.9+
• External dependencies: requests>=2.31.0, jsonschema>=4.19.0
• A Genesys Cloud organization with LLM Gateway enabled and webhook endpoint configured for external sync

Authentication Setup

Genesys Cloud uses bearer token authentication. The following class handles token acquisition, caching, and automatic refresh before expiration.

import requests
import time
import json
from typing import Optional

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, org_id: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.org_id = org_id
        self.token_url = f"https://login.mypurecloud.com/oauth/token"
        self.access_token: Optional[str] = None
        self.expires_at: float = 0.0

    def get_token(self) -> str:
        if self.access_token and time.time() < self.expires_at:
            return self.access_token
        
        payload = {
            "grant_type": "client_credentials",
            "scope": "ai:llm:function-definition:read ai:llm:function-definition:write"
        }
        headers = {"Content-Type": "application/x-www-form-urlencoded"}
        auth = (self.client_id, self.client_secret)
        
        response = requests.post(self.token_url, data=payload, headers=headers, auth=auth)
        response.raise_for_status()
        
        data = response.json()
        self.access_token = data["access_token"]
        self.expires_at = time.time() + data["expires_in"] - 30
        return self.access_token

OAuth scope required for all subsequent operations: ai:llm:function-definition:read ai:llm:function-definition:write

Implementation

Step 1: Constructing Definition Payloads with Parameter Schema Matrices

Genesys Cloud expects function definitions to contain a function ID reference, a JSON Schema parameter matrix, and execution timeout directives. The payload must match the platform schema exactly to prevent parsing failures during AI routing.

from typing import Dict, Any, List

def build_function_definition(
    function_id: str,
    name: str,
    description: str,
    parameters: Dict[str, Any],
    timeout_millis: int = 5000
) -> Dict[str, Any]:
    return {
        "id": function_id,
        "name": name,
        "description": description,
        "timeoutMillis": timeout_millis,
        "parameters": {
            "type": "object",
            "properties": parameters,
            "required": list(parameters.keys())
        },
        "version": 1
    }

# Example parameter schema matrix
weather_params = {
    "location": {"type": "string", "description": "City name or GPS coordinates"},
    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "default": "celsius"},
    "include_forecast": {"type": "boolean", "default": False}
}

definition_payload = build_function_definition(
    function_id="func-weather-v1",
    name="GetWeatherData",
    description="Retrieves current weather conditions for a specified location",
    parameters=weather_params,
    timeout_millis=4500
)

HTTP Request Cycle:

• Method: PUT
• Path: /api/v2/ai/llm/function-definitions/func-weather-v1
• Headers: Authorization: Bearer <token>, Content-Type: application/json
• Body: The definition_payload dictionary above
• Expected Response (200 OK):

{
  "id": "func-weather-v1",
  "name": "GetWeatherData",
  "description": "Retrieves current weather conditions for a specified location",
  "timeoutMillis": 4500,
  "parameters": {
    "type": "object",
    "properties": {
      "location": {"type": "string", "description": "City name or GPS coordinates"},
      "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "default": "celsius"},
      "include_forecast": {"type": "boolean", "default": false}
    },
    "required": ["location", "unit", "include_forecast"]
  },
  "version": 1,
  "selfUri": "/api/v2/ai/llm/function-definitions/func-weather-v1",
  "etag": "a3f8c9d2-11b4-4e5a-9c7f-88d44e12a001"
}

Step 2: Validating Schemas Against Constraints and Security Policies

Before persistence, the definition must pass JSON schema validation, maximum parameter count limits, schema projection analysis, and security policy verification. This prevents injection attacks and ensures safe function iteration during LLM execution.

import jsonschema
from jsonschema import Draft7Validator, SchemaError
from typing import Dict, Any, Set

MAX_PARAMETERS = 10
ALLOWED_TYPES: Set[str] = {"string", "number", "integer", "boolean", "array", "object"}
MAX_NESTING_DEPTH = 3

def validate_json_schema(schema: Dict[str, Any]) -> bool:
    try:
        Draft7Validator.check_schema(schema)
    except SchemaError as e:
        raise ValueError(f"JSON Schema validation failed: {e}")
    return True

def analyze_schema_projection(schema: Dict[str, Any], depth: int = 1) -> bool:
    if depth > MAX_NESTING_DEPTH:
        raise ValueError(f"Schema projection exceeds maximum nesting depth of {MAX_NESTING_DEPTH}")
    
    props = schema.get("properties", {})
    for key, value in props.items():
        if value.get("type") == "object" and "properties" in value:
            analyze_schema_projection(value, depth + 1)
    return True

def verify_security_policy(parameters: Dict[str, Any]) -> bool:
    if len(parameters) > MAX_PARAMETERS:
        raise ValueError(f"Parameter count {len(parameters)} exceeds limit {MAX_PARAMETERS}")
    
    for key, param in parameters.items():
        ptype = param.get("type")
        if ptype not in ALLOWED_TYPES:
            raise ValueError(f"Disallowed parameter type '{ptype}' for '{key}'. Allowed: {ALLOWED_TYPES}")
        
        if ptype == "string" and "maxLength" not in param:
            param["maxLength"] = 255
            
        if ptype == "enum":
            raise ValueError("Top-level enum types are not permitted in function parameters")
    return True

def validate_definition_payload(payload: Dict[str, Any]) -> bool:
    param_schema = payload.get("parameters", {})
    props = param_schema.get("properties", {})
    
    validate_json_schema(param_schema)
    verify_security_policy(props)
    analyze_schema_projection(param_schema)
    
    return True

OAuth scope required: ai:llm:function-definition:read (validation occurs client-side before write)

Step 3: Persisting Definitions with Optimistic Locking and Atomic PUT

Genesys Cloud enforces optimistic locking via the If-Match header. The API returns an etag on GET or PUT responses. Subsequent updates must include this value to prevent concurrent modification conflicts. The following function handles the atomic PUT with automatic retry for 429 rate limits.

import time
import requests
from typing import Optional

def put_function_definition(
    base_url: str,
    token: str,
    definition: Dict[str, Any],
    etag: Optional[str] = None,
    max_retries: int = 3
) -> Dict[str, Any]:
    url = f"{base_url}/api/v2/ai/llm/function-definitions/{definition['id']}"
    headers = {
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json",
        "Accept": "application/json"
    }
    if etag:
        headers["If-Match"] = etag

    for attempt in range(max_retries):
        response = requests.put(url, headers=headers, json=definition)
        
        if response.status_code == 429:
            retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
            time.sleep(retry_after)
            continue
            
        if response.status_code == 412:
            raise RuntimeError("Optimistic lock conflict. Another process modified the definition.")
            
        if response.status_code in (401, 403):
            raise PermissionError(f"Authentication or authorization failed: {response.status_code}")
            
        response.raise_for_status()
        return response.json()
        
    raise RuntimeError("Max retries exceeded for 429 rate limit")

HTTP Request Cycle:

• Method: PUT
• Path: /api/v2/ai/llm/function-definitions/func-weather-v1
• Headers: Authorization: Bearer <token>, Content-Type: application/json, If-Match: a3f8c9d2-11b4-4e5a-9c7f-88d44e12a001
• Body: Validated definition payload
• Expected Response (200 OK): Updated definition with new etag and incremented version

Step 4: Synchronizing Changes, Tracking Latency, and Generating Audit Logs

Definition changes must synchronize with external orchestration platforms via webhook callbacks. Latency tracking and audit logging provide governance compliance and developer efficiency metrics.

import logging
from datetime import datetime, timezone
from typing import Dict, Any

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger("llm-configurator")

def track_latency(operation: str, start_time: float) -> float:
    elapsed = time.time() - start_time
    logger.info(f"Latency [{operation}]: {elapsed:.3f}s")
    return elapsed

def log_audit(action: str, definition_id: str, status: str, details: str = "") -> Dict[str, Any]:
    audit_entry = {
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "action": action,
        "definition_id": definition_id,
        "status": status,
        "details": details
    }
    logger.info(f"AUDIT_LOG: {json.dumps(audit_entry)}")
    return audit_entry

def sync_webhook(webhook_url: str, payload: Dict[str, Any]) -> None:
    try:
        response = requests.post(
            webhook_url,
            json={"event": "FUNCTION_DEFINITION_UPDATED", "data": payload},
            headers={"Content-Type": "application/json"},
            timeout=5
        )
        response.raise_for_status()
        logger.info(f"Webhook sync successful for {payload.get('id')}")
    except requests.RequestException as e:
        logger.warning(f"Webhook sync failed: {e}")

OAuth scope required: None (external sync occurs outside Genesys authentication boundary)

Complete Working Example

The following module combines authentication, validation, persistence, latency tracking, audit logging, and webhook synchronization into a single configurable class. Copy the script, insert your credentials, and execute.

import requests
import time
import json
import logging
from datetime import datetime, timezone
from typing import Dict, Any, Optional, Set

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger("llm-configurator")

MAX_PARAMETERS = 10
ALLOWED_TYPES: Set[str] = {"string", "number", "integer", "boolean", "array", "object"}
MAX_NESTING_DEPTH = 3

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, org_id: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://login.mypurecloud.com/oauth/token"
        self.access_token: Optional[str] = None
        self.expires_at: float = 0.0

    def get_token(self) -> str:
        if self.access_token and time.time() < self.expires_at:
            return self.access_token
        payload = {
            "grant_type": "client_credentials",
            "scope": "ai:llm:function-definition:read ai:llm:function-definition:write"
        }
        headers = {"Content-Type": "application/x-www-form-urlencoded"}
        auth = (self.client_id, self.client_secret)
        response = requests.post(self.token_url, data=payload, headers=headers, auth=auth)
        response.raise_for_status()
        data = response.json()
        self.access_token = data["access_token"]
        self.expires_at = time.time() + data["expires_in"] - 30
        return self.access_token

class LLMFunctionConfigurator:
    def __init__(self, base_url: str, auth: GenesysAuth, webhook_url: str):
        self.base_url = base_url
        self.auth = auth
        self.webhook_url = webhook_url
        self.success_count: int = 0
        self.total_attempts: int = 0

    def _track_latency(self, operation: str, start_time: float) -> float:
        elapsed = time.time() - start_time
        logger.info(f"Latency [{operation}]: {elapsed:.3f}s")
        return elapsed

    def _log_audit(self, action: str, definition_id: str, status: str, details: str = "") -> Dict[str, Any]:
        audit_entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "action": action,
            "definition_id": definition_id,
            "status": status,
            "details": details
        }
        logger.info(f"AUDIT_LOG: {json.dumps(audit_entry)}")
        return audit_entry

    def _validate_payload(self, payload: Dict[str, Any]) -> bool:
        import jsonschema
        param_schema = payload.get("parameters", {})
        props = param_schema.get("properties", {})

        jsonschema.Draft7Validator.check_schema(param_schema)

        if len(props) > MAX_PARAMETERS:
            raise ValueError(f"Parameter count {len(props)} exceeds limit {MAX_PARAMETERS}")

        for key, param in props.items():
            ptype = param.get("type")
            if ptype not in ALLOWED_TYPES:
                raise ValueError(f"Disallowed parameter type '{ptype}' for '{key}'")
            if ptype == "string" and "maxLength" not in param:
                param["maxLength"] = 255

        def check_depth(schema: Dict[str, Any], depth: int = 1) -> None:
            if depth > MAX_NESTING_DEPTH:
                raise ValueError(f"Schema projection exceeds maximum nesting depth of {MAX_NESTING_DEPTH}")
            for val in schema.get("properties", {}).values():
                if val.get("type") == "object" and "properties" in val:
                    check_depth(val, depth + 1)

        check_depth(param_schema)
        return True

    def _sync_webhook(self, payload: Dict[str, Any]) -> None:
        try:
            response = requests.post(
                self.webhook_url,
                json={"event": "FUNCTION_DEFINITION_UPDATED", "data": payload},
                headers={"Content-Type": "application/json"},
                timeout=5
            )
            response.raise_for_status()
            logger.info(f"Webhook sync successful for {payload.get('id')}")
        except requests.RequestException as e:
            logger.warning(f"Webhook sync failed: {e}")

    def persist_definition(self, definition: Dict[str, Any], etag: Optional[str] = None, max_retries: int = 3) -> Dict[str, Any]:
        self.total_attempts += 1
        start_time = time.time()
        token = self.auth.get_token()
        url = f"{self.base_url}/api/v2/ai/llm/function-definitions/{definition['id']}"
        
        headers = {
            "Authorization": f"Bearer {token}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }
        if etag:
            headers["If-Match"] = etag

        for attempt in range(max_retries):
            response = requests.put(url, headers=headers, json=definition)
            
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
                time.sleep(retry_after)
                continue
            if response.status_code == 412:
                self._log_audit("PUT_DEFINITION", definition["id"], "FAILED", "Optimistic lock conflict")
                raise RuntimeError("Optimistic lock conflict. Another process modified the definition.")
            if response.status_code in (401, 403):
                self._log_audit("PUT_DEFINITION", definition["id"], "FAILED", f"Auth error: {response.status_code}")
                raise PermissionError(f"Authentication or authorization failed: {response.status_code}")
                
            response.raise_for_status()
            result = response.json()
            
            self.success_count += 1
            latency = self._track_latency("PUT_DEFINITION", start_time)
            self._log_audit("PUT_DEFINITION", definition["id"], "SUCCESS", f"Latency: {latency:.3f}s, Version: {result.get('version')}")
            self._sync_webhook(result)
            return result

        raise RuntimeError("Max retries exceeded for 429 rate limit")

    def get_validation_success_rate(self) -> float:
        if self.total_attempts == 0:
            return 0.0
        return (self.success_count / self.total_attempts) * 100

if __name__ == "__main__":
    # Configuration
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    ORG_ID = "your_org_id"
    BASE_URL = "https://api.mypurecloud.com"
    WEBHOOK_URL = "https://your-orchestration-platform.com/webhooks/genesys-llm-sync"

    auth = GenesysAuth(CLIENT_ID, CLIENT_SECRET, ORG_ID)
    configurator = LLMFunctionConfigurator(BASE_URL, auth, WEBHOOK_URL)

    # Construct payload
    payload = {
        "id": "func-currency-convert",
        "name": "ConvertCurrency",
        "description": "Converts monetary values between supported currencies",
        "timeoutMillis": 3000,
        "parameters": {
            "type": "object",
            "properties": {
                "amount": {"type": "number", "description": "Monetary value to convert"},
                "from_currency": {"type": "string", "enum": ["USD", "EUR", "GBP"], "default": "USD"},
                "to_currency": {"type": "string", "enum": ["USD", "EUR", "GBP"], "default": "EUR"}
            },
            "required": ["amount", "from_currency", "to_currency"]
        },
        "version": 1
    }

    try:
        # Validate before persistence
        configurator._validate_payload(payload)
        
        # Persist with optimistic locking (etag can be retrieved via GET if updating existing)
        result = configurator.persist_definition(payload, etag=None)
        print(f"Definition persisted successfully. New version: {result['version']}")
        print(f"Validation success rate: {configurator.get_validation_success_rate():.2f}%")
    except Exception as e:
        logger.error(f"Configuration failed: {e}")
        raise

Common Errors & Debugging

Error: 401 Unauthorized or 403 Forbidden

• What causes it: The OAuth token has expired, the client credentials are incorrect, or the token lacks the required ai:llm:function-definition:write scope.
• How to fix it: Verify the scope string in the token request matches exactly. Ensure the application user has the LLM Gateway admin role. The authentication class automatically refreshes tokens before expiration.
• Code showing the fix: The GenesysAuth.get_token() method validates expiration and re-fetches tokens. The persist_definition method raises a clear PermissionError on 401/403 responses.

Error: 409 Conflict or 412 Precondition Failed

• What causes it: Optimistic locking conflict. Another process or admin user modified the definition after you retrieved the ETag.
• How to fix it: Fetch the latest definition using a GET request, merge your changes into the returned payload, and retry the PUT with the new ETag.
• Code showing the fix: The persist_definition method checks for 412 status codes and raises a descriptive error. Implement a retry loop that calls GET /api/v2/ai/llm/function-definitions/{id} to retrieve the fresh ETag before retrying.

Error: 429 Too Many Requests

• What causes it: Genesys Cloud API rate limits exceeded during bulk definition updates.
• How to fix it: Implement exponential backoff with jitter. The retry loop in persist_definition reads the Retry-After header or falls back to 2 ** attempt seconds.
• Code showing the fix: Included in the persist_definition retry block. The loop pauses execution and retries up to max_retries times before failing.

Error: JSON Schema Validation Failure

• What causes it: Parameter definitions contain unsupported types, exceed the maximum parameter count, or violate nesting depth limits.
• How to fix it: Run _validate_payload before calling the API. The method enforces MAX_PARAMETERS, ALLOWED_TYPES, and MAX_NESTING_DEPTH. Adjust the schema to match allowed constraints.
• Code showing the fix: The _validate_payload method raises ValueError with explicit messages indicating which constraint failed.

Official References

• Genesys Cloud LLM Function Definitions API Documentation
• Genesys Cloud OAuth 2.0 Client Credentials Flow
• JSON Schema Validation Specification
• Genesys Cloud Python SDK Reference